license: apache-2.0
datasets:
- openclimatefix/era5
language:
- es
- en
metrics:
- mse
library_name: transformers
pipeline_tag: image-to-image
tags:
- climate
- transformers
- super-resolution
Europe Reanalysis Super Resolution
The aim of the project is to create a Machine learning (ML) model that can generate high-resolution regional reanalysis data (similar to the one produced by CERRA) by downscaling global reanalysis data from ERA5.
This will be accomplished by using state-of-the-art Deep Learning (DL) techniques like U-Net, conditional GAN, and diffusion models (among others). Additionally, an ingestion module will be implemented to assess the possible benefit of using CERRA pseudo-observations as extra predictors. Once the model is designed and trained, a detailed validation framework takes the place.
It combines classical deterministic error metrics with in-depth validations, including time series, maps, spatio-temporal correlations, and computer vision metrics, disaggregated by months, seasons, and geographical regions, to evaluate the effectiveness of the model in reducing errors and representing physical processes. This level of granularity allows for a more comprehensive and accurate assessment, which is critical for ensuring that the model is effective in practice.
Moreover, tools for interpretability of DL models can be used to understand the inner workings and decision-making processes of these complex structures by analyzing the activations of different neurons and the importance of different features in the input data.
This work is funded by Code for Earth 2023 initiative.
Table of Contents
- Model Card for Europe Reanalysis Super Resolution
- Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Model Examination
- Technical Specifications [optional]
- Authors
Model Details
Model Description
A vision model for down-scaling (from 0.25º to 0.05º) regional reanalysis grids in the mediterranean area.
- Developed by: A team of Predictia Intelligent Data Solutions S.L. & Instituto de Fisica de Cantabria (IFCA)
- Model type: Vision model
- Language(s) (NLP): en, es
- License: Apache-2.0
- Resources for more information: More information needed
Uses
Direct Use
Downstream Use [Optional]
Out-of-Scope Use
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Training Details
Training Data
The dataset used is a composition of the ERA5 and CERRA reanalysis.
The spatial coverage of the input grids (ERA5) is defined below, and corresponds to a 2D array of dimensions (60, 42):
longitude: [-8.35, 6.6]
latitude: [46.45, 35.50]
On the other hand, the target high-resolution grid (CERRA) correspond to a 2D matrix of dimmension (240, 160):
longitude: [-6.85, 5.1]
latitude: [44.95, 37]
The data samples used for training corresponds to the period from 1981 and 2013 (both included) and from 2014 to 2017 for per-epoch validation.
Training Procedure
Preprocessing
More information needed
Speeds, Sizes, Times
More information needed
Evaluation
Testing Data, Factors & Metrics
Testing Data
The dataset used is a composition of the ERA5 and CERRA reanalysis.
The spatial coverage of the input grids (ERA5) and the target high-resolution grids (CERRA) is fixed for both training and testing.
The data samples used for testing corresponds to the period from 2018 to 2020 (both included).
Factors
More information needed
Metrics
More information needed
Results
More information needed
Model Examination
More information needed
Technical Specifications [optional]
Model Architecture and Objective
More information needed
Compute Infrastructure
More information needed
Hardware
More information needed
Software
More information needed
Authors
Mario Santa Cruz. Predictia Intelligent Data Solutions S.L.
Antonio Pérez. Predictia Intelligent Data Solutions S.L.
Javier Díez. Predictia Intelligent Data Solutions S.L.